Quantum chemical modelling of sequence encoded energetics and electrostatic landscapes governing early peptide association in α- synuclein and amyloid β
Quantum chemical modelling of sequence encoded energetics and electrostatic landscapes governing early peptide association in α- synuclein and amyloid β
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Publication status: preprint
A plain-language reading has not been prepared for this paper yet.
Original abstract
Abstract Context Early molecular association of α-synuclein and amyloid β (Aβ) underlies the formation of higher order assemblies implicated in Parkinson’s and Alzheimer’s diseases, yet how residue order shapes the molecular properties governing these initial association events remains poorly resolved. Here, aggregation prone peptide segments were compared with composition matched scrambled controls to isolate the contribution of sequence arrangement while conserving amino acid composition. Residue rearrangement produced distinct energetic responses in the two peptide systems. Aggregation prone Aβ exhibited an association energy of 115.617 kJ/mol compared with 232.552 kJ/mol for its control, whereas aggregation prone α-synuclein exhibited − 172.819 kJ/mol compared with − 494.371 kJ/mol for its control. These energetic differences were accompanied by contrasting intermolecular organization: aggregation prone α-synuclein formed 166 heavy atom contacts compared with 105 for its control, whereas aggregation prone Aβ formed 40 compared with 81. Residue rearrangement further reorganized molecular electrostatic potential (ESP) landscapes and Hirshfeld atomic charge distributions, while sequence descriptors revealed peptide specific changes in charge patterning, hydrophobic organization and molecular compactness. Together, these results established residue order as a determinant of early peptide association and revealed that identical amino acid compositions can generate distinct energetic, structural and electrostatic characteristics through sequence dependent molecular organization. Method Monomer and homodimer structures were optimized using the GFN2-xTB method implemented in xTB, with initial dimer relaxation using the GFN force field. Association energies were calculated from the converged GFN2-xTB electronic energies of the independently optimized monomers and corresponding dimers. DFT calculations were performed using GPAW with the PBE exchange correlation functional and PAW formalism on a real space grid with 0.20 Å spacing. ESP and Hirshfeld atomic charge analyses were obtained from the converged electronic densities. Sequence, structural and intermolecular interface descriptors were calculated using Python with the ASE, NumPy, SciPy, pandas, Matplotlib and scikit image. Open Babel was used for molecular structure conversion.